Estimating grazing land acres across the contiguous United States using machine learning methods

Authors: HU, MINGYUE - IOWA STATE UNIVERSITY; YU, CINDY - IOWA STATE UNIVERSITY; ZHU, ZHENGYUAN - IOWA STATE UNIVERSITY; MCCORD, SARAH; METZ, LORETTA - RETIRED NON ARS EMPLOYEE

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/26/2026
Publication Date: 3/31/2026
Citation: Hu, M., Yu, C., Zhu, Z., McCord, S.E., Metz, L.J. 2026. Estimating grazing land acres across the contiguous United States using machine learning methods. Remote Sensing. 18(7):1050. https://doi.org/10.3390/rs18071050.
DOI: https://doi.org/10.3390/rs18071050

Interpretive Summary: This research provides a robust framework for estimating rangeland and pastureland acreages, allowing for expanded application of the NRI data to conservation, modeling, and ecological uses. Prior to this method, the NRI data had limited use and application in many grazing land studies because the scales of interpretation, reporting and conservation application were too coarse. With the new ability to create acreage estimates for user-defined geographies, the data is wholly relevant and useful for applications such as ecological site descriptions, watershed planning, hazard estimation and risk reduction targeting, modeling ecological outcomes at hillslope or edge of field scales, conservation planning within ecologically-relevant regions, and quantifying trends in land use change, invasive species expansion, forage production variability, potential threats from increases in bare ground or other indicators, and spatiotemporal shifts in soil, water, air, plant and animal resource concerns on non-federal rangelands and pasturelands.

Technical Abstract: Quantifying the extent of rangeland and pastureland (collectively termed grazing lands herein) in the US is a critical first step in many grazing lands assessments. This research presents a model-assisted framework to estimate grazing land acreage within arbitrary geographic boundaries by integrating high quality survey data with satellite-based raster geospatial data. Leveraging the image photo interpretation data from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) survey as a reference dataset, we use machine learning to fuse NRI point level data with auxiliary data from the satellite-based Cropland Data Layer (CDL) to enhance the precision of acreage estimates of grazing lands. The methodology includes three steps: (1) modeling the relationship between NRI rangeland and pastureland indicators and CDL variables; (2) generating a high-resolution rangeland and pastureland probabilities map across the contiguous US; and (3) summarizing these probabilities to calculate the acreage of rangeland and pastureland for specific areas of interest. This approach provides researchers and land managers with a scalable tool to define grazing land extents within a self-selected study area, ensuring that subsequent resource characteristics or condition assessments are representative and spatially accurate.